With the steady increase in 3D sensors availability, point clouds have seen a rapid diffusion and adoption.
Their use benefits from the 3D information that they readily express, which allows to easily retrieve geometric properties and yield the possibility to extract complex geometric features to classify object shapes, perform scene segmentation, or assess complex situations.
Point clouds are also used to solve motion estimation and self-localization problems in robotics.
Scan registration algorithms provide an estimate of the 3D transformation between the positions at which two point clouds are acquired.
Such processes are at the heart of LiDAR-based odometry [1]} and SLAM frameworks [2]}.
Originally introduced in [3]}, the Iterative Closest Point (ICP) algorithm, in its many variants, gradually became the standard approach to the point-cloud registration problem [4]}.
